Operations | Monitoring | ITSM | DevOps | Cloud

It was 2am and I was paying for the privilege. Something was on fire in production, and I’d done the modern thing: I pointed an AI agent at it. It ingested the dashboards. It read the logs. It walked the traces. Then it handed me back a beautifully formatted paragraph that said, in effect, “latency is elevated on the checkout path.” I knew that. The page told me that.

Polling has been the go-to approach for network monitoring for years, and it still plays an important role in keeping networks healthy. But as networks become more distributed, application-driven, and data-intensive, simply polling devices more often isn't always the most efficient way to gain deeper operational insights. That's where network telemetry comes in.

Coralogix and Sumo Logic are two different answers to the same observability platform decision. Where Coralogix processes telemetry in flight, stores it in your own Amazon Simple Storage Service (S3) bucket, and prices on data ingested, Sumo Logic keeps data in vendor-managed storage and, under its Flex model, bills for data scanned at query time. Both platforms have introduced pricing and artificial intelligence (AI) changes in the past year, and those changes have widened the difference between them.

Coralogix and New Relic both cover the full observability surface, but they charge for it and store it in different ways. One prices purely on data ingested and writes telemetry to a bucket you own, while the other combines ingest pricing with per-user licensing and retains data in its own backend. This guide covers how the two platforms compare on core features, pricing structure, AI observability, archiving and retention, security coverage, and support, then shows when each one is the stronger choice.

When something goes down, the last thing you want is to alt-tab between a monitoring dashboard, your on-call tool, and three Slack threads to figure out what is happening and who owns it. That context is usually all there. It is just scattered. The Hyperping MCP server fixes that by putting your monitoring data inside the AI tools you already work in. Your agent can read monitor state, outage timelines, SLAs, and on-call schedules, and answer the questions you would normally chase across tabs.

Knowing how to audit different types of IT hardware matters because a laptop, a server, and a network switch fail an audit for completely different reasons. Treating every device the same way during an audit means missing the checks that actually matter for each category, from disk encryption on an endpoint to firmware version on a router.

In our previous post on AI tokenomics, we looked at the rising cost challenge behind token-based AI systems. When enterprise IT teams rely on AI to reason through the same repeatable work over and over again, the costs to resolve those tasks may increase to an unreasonable level. That is where a deterministic IT automation platform becomes essential. A deterministic workflow follows predefined logic, meaning that given the same inputs and conditions, it produces the same expected result.

When technology works, businesses thrive. Employees stay productive, customers stay connected, and critical services keep running. But when something goes wrong, the real challenge is not only detecting the issue. It is understanding what it affects, who may fell the impact, and how urgently the business needs to respond. That is the value behind the Kyoto release. The latest Skylar One update helps teams better connect IT health to business impact.

Every layer of the modern network stack moves at cloud speed. If your connectivity doesn't, your entire strategy can stall. Co-authored by Fabio D’Avino This blog includes insights from Fabio D’Avino, a specialist in Network as a Service (NaaS) with more than seven years of experience researching, designing, and building global network services. Fabio’s work explores how organizations can modernize connectivity as cloud, hybrid, and AI-ready infrastructure strategies evolve.

Many times, the hardest part of troubleshooting isn’t fixing the actual problem. It’s figuring out where to start. As engineers, it’s easy to lose count of how many times we’ve opened logs, then 10 metrics tabs, and another 10 tabs with trace queries, only to end up back in the logs trying to find a root cause.